The impact of training strategies on the accuracy of genomic predictors in United States Red Angus cattle

Lee, J.; Kachman, S. D.; Spangler, M. L.

doi:10.2527/jas2017.1604

Cited by 4 publications

(7 citation statements)

References 26 publications

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“…Interestingly, the largest difference (+8.3%) in terms of average accuracies of genomic prediction between the two response variables was observed for the lowest heritable trait (LMA) when using DEBVincPA as a response variable. While DEBVexcPA [15] has the greatest numerical properties in addressing double counting by removing the parental contribution [8,33,34], our results showed a lower performance in prediction accuracies in comparisons of two response variables. Boddhireddy et al [34] reported that using EBV without removing parental contributions as a response variable yielded greater prediction accuracies compared to using DEBVexcPA in both validation tests for US Angus beef cattle.…”

Section: Response Variables (Debvincpa and Debvexcpa)mentioning

confidence: 61%

“…Pérez-Enciso et al [7] observed that the reliabilities of genomic predictions did not increase when using a high-density SNP chip (HD) compared with a 50K SNP chip. Lee et al [8] and Guo et al [33] also reported no significant improvement in accuracy when using a 50K panel vs an 80K panel for Red Angus beef cattle in the United States. Overall, no significant improvements in prediction accuracies on the basis of SNP panel density have been observed from the results of previous genomic prediction studies (50K vs. 777K or 50K vs. 80K) because even though the number of SNPs increases, the panel may contain a small number of SNP markers in high LD with causative variants.…”

Section: Genome-wide Association Study (Gwas) For Growth-and Productimentioning

confidence: 99%

“…Overall, no significant improvements in prediction accuracies on the basis of SNP panel density have been observed from the results of previous genomic prediction studies (50K vs. 777K or 50K vs. 80K) because even though the number of SNPs increases, the panel may contain a small number of SNP markers in high LD with causative variants. In addition, simply increasing SNP markers instead of causal mutation may bring an additional source of noise to genomic prediction [5,7,8]. The results revealed a slight increase in genomic accuracy for DAYS and LMA with the BayesB method and the GeneSeek80K SNP platform compared with the Illumina60K SNP platform.…”

Section: Genome-wide Association Study (Gwas) For Growth-and Productimentioning

confidence: 99%

“…To account for the relatively small sample size of the prediction model, a 10-fold cross-validation strategy was used to estimate the accuracies of the genomic prediction models. Previous study related to the number of folds on the process of the cross validation have reported that trade-off effects were detected between the number of folds and the relationships between training and testing sets [8]. Nevertheless, we used a 10-fold cross-validation to maximize the size of the training data because of the limited reference data set in Korean Duroc pigs.…”

Section: Accuracy Of Genomic Prediction Under a 10-fold Cross-validationmentioning

confidence: 99%

“…Breeders of various animal species have conducted research using various prediction models with the aim of increasing the accuracy of genomic prediction for more reliable GE-EBVs. However, these models are affected by several factors, such as the size of the reference data, which mean having both genomic and phenotypic data [3,4], density of genotyping platforms [5][6][7][8], relationships between training and testing sets in the process of cross validation for genomic accuracy [9], and choice of response variables in genomic prediction models [8]. In addition, the models were also affected by the choice of covariates (individual SNP vs. haplotype) in genomic prediction modeling [10], choice of penalty or a priori density in statistical methods (e.g., regression on SNP marker) [11], and causative variants or SNPs in strong linkage disequilibrium (LD) with causative variants [12].…”

Section: Introductionmentioning

confidence: 99%

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Genomic Analysis Using Bayesian Methods under Different Genotyping Platforms in Korean Duroc Pigs

Lee¹,

Kim

Cho

et al. 2020

Animals

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Genomic evaluation has been widely applied to several species using commercial single nucleotide polymorphism (SNP) genotyping platforms. This study investigated the informative genomic regions and the efficiency of genomic prediction by using two Bayesian approaches (BayesB and BayesC) under two moderate-density SNP genotyping panels in Korean Duroc pigs. Growth and production records of 1026 individuals were genotyped using two medium-density, SNP genotyping platforms: Illumina60K and GeneSeek80K. These platforms consisted of 61,565 and 68,528 SNP markers, respectively. The deregressed estimated breeding values (DEBVs) derived from estimated breeding values (EBVs) and their reliabilities were taken as response variables. Two Bayesian approaches were implemented to perform the genome-wide association study (GWAS) and genomic prediction. Multiple significant regions for days to 90 kg (DAYS), lean muscle area (LMA), and lean percent (PCL) were detected. The most significant SNP marker, located near the MC4R gene, was detected using GeneSeek80K. Accuracy of genomic predictions was higher using the GeneSeek80K SNP panel for DAYS (Δ2%) and LMA (Δ2–3%) with two response variables, with no gains in accuracy by the Bayesian approaches in four growth and production-related traits. Genomic prediction is best derived from DEBVs including parental information as a response variable between two DEBVs regardless of the genotyping platform and the Bayesian method for genomic prediction accuracy in Korean Duroc pig breeding.

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Section: Response Variables (Debvincpa and Debvexcpa)mentioning

confidence: 61%

Section: Genome-wide Association Study (Gwas) For Growth-and Productimentioning

confidence: 99%

Section: Genome-wide Association Study (Gwas) For Growth-and Productimentioning

confidence: 99%

Section: Accuracy Of Genomic Prediction Under a 10-fold Cross-validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Genomic Analysis Using Bayesian Methods under Different Genotyping Platforms in Korean Duroc Pigs

Lee¹,

Kim

Cho

et al. 2020

Animals

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Knowledge mapping analysis of the development of molecular breeding technology for beef cattle in China

Li,

Zhou,

et al. 2024

Applied Mathematics and Nonlinear Sciences

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With China’s economic and social development, material living standards have improved, leading to a dramatic increase in the demand for beef and higher requirements for its quality. In this context, molecular breeding technology for beef cattle has gradually become a research hotspot. This paper designs the entire process of knowledge mapping for molecular breeding technology of beef cattle in China, focusing on the molecular breeding knowledge extraction model based on BERT. It also integrates models and algorithms used in knowledge alignment, complementation, and storage to construct the knowledge graph. After testing the performance of the knowledge extraction algorithm, the development history of molecular breeding technology for beef cattle in China was analyzed. The results show that from 2013 to 2023, research related to molecular breeding technology for beef cattle in China exhibited a slow, then rapid growth trend. The terms “molecular breeding” and “beef cattle” appeared most frequently, with 218 and 165 occurrences, respectively, and had the highest centrality. Keywords with high occurrences include “comparative genes” (11.3068), “functional genes” (9.2376), “genome-wide association” (8.3197), and “genome-wide selection” (6.9012), all closely related to the development of molecular breeding technology for beef cattle in China. The prediction of future trends in molecular breeding technology for beef cattle in China is significantly influenced by this study.

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Estimation of Variance Components and Genomic Prediction for Individual Birth Weight Using Three Different Genome-Wide SNP Platforms in Yorkshire Pigs

Lee

Lim

et al. 2020

Animals

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This study estimates the individual birth weight (IBW) trait heritability and investigates the genomic prediction efficiency using three types of high-density single nucleotide polymorphism (SNP) genotyping panels in Korean Yorkshire pigs. We use 38,864 IBW phenotypic records to identify a suitable model for statistical genetics, where 698 genotypes match our phenotypic records. During our genomic analysis, the deregressed estimated breeding values (DEBVs) and their reliabilities are used as derived response variables from the estimated breeding values (EBVs). Bayesian methods identify the informative regions and perform the genomic prediction using the IBW trait, in which two common significant window regions (SSC8 27 Mb and SSC15 29 Mb) are identified using the three genotyping platforms. Higher prediction ability is observed using the DEBV-including parent average as a response variable, regardless of the SNP genotyping panels and the Bayesian methods, relative to the DEBV-excluding parent average. Hence, we suggest that fine-mapping studies targeting the identified informative regions in this study are necessary to find the causal mutations to improve the IBW trait’s prediction ability. Furthermore, studying the IBW trait using a genomic prediction model with a larger genomic dataset may improve the genomic prediction accuracy in Korean Yorkshire pigs.

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The impact of training strategies on the accuracy of genomic predictors in United States Red Angus cattle

Cited by 4 publications

References 26 publications

Genomic Analysis Using Bayesian Methods under Different Genotyping Platforms in Korean Duroc Pigs

Genomic Analysis Using Bayesian Methods under Different Genotyping Platforms in Korean Duroc Pigs

Knowledge mapping analysis of the development of molecular breeding technology for beef cattle in China

Estimation of Variance Components and Genomic Prediction for Individual Birth Weight Using Three Different Genome-Wide SNP Platforms in Yorkshire Pigs

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